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The Role of STEM Occupations in the German Labor Market Alexandra Spitz-Oener Humboldt-University Berlin and IAB, Nuremberg Based on joint work with Kai Priesack (VDI/VDE Innovation + Technik GmbH) IAB-OECD Seminar: Rising Wage Inequality in Germany December 16/17, 2018

The Role of STEM Occupations in the German Labor Market role of STEM occupations... · 2019. 1. 25. · The Role of STEM Occupations in the German Labor Market Alexandra Spitz-Oener

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  • The Role of STEM Occupations in the German Labor Market

    Alexandra Spitz-Oener

    Humboldt-University Berlin and IAB, Nuremberg

    Based on joint work with Kai Priesack (VDI/VDE Innovation + Technik GmbH)

    IAB-OECD Seminar: Rising Wage Inequality in GermanyDecember 16/17, 2018

  • Motivation

    • Science, Technology, Engineering and Mathematics (STEM) occupations high on political agenda in industrialized countries

    • High-skilled labor shortage is a concern, in particular shortage of workers with STEM skills

    • Growing literature on different aspects of STEM jobs (e.g. education, immigration, gender)

    • Evolution of STEM employment and drivers of ‘STEM premium’ not systematically studied so far

  • Today’s Agenda

    1) How did STEM employment and wages in West Germany develop over time (1980-2010)?

    2) What drives wages in STEM occupations?

    i. Supply and Demand (competitive labor market)

    ii. Quality of workers, quality of firms, and assortative matching?

    (imperfectly competitive labor markets)

  • Main Findings

    1) Positive STEM wage premium, with accelerated premium growth since mid-1990s for both men and women

    2) Evidence for growth of STEM premium driven by demand outpacing supply for STEM workers

    3) Quality of worker (skills and other factors) explain largest part of STEM wage premium

    4) Firm quality increased in importance, and is major component of acceleration in premium growth over time

  • Broad Labor Market Trends:Indexed Wage Growth (1980-2010)

    Men Women

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.

  • STEM Occupations(U.S. Census STEM Jobs Definition)

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.

  • STEM Occupations(Changes in Employment Shares, 1980-2010)

    Men Women

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.Note: Figures in parenthesis indicate position in 1980 male wage distribution.

  • Employment Polarization (1980-2010):Contribution of Non-STEM/STEM

    Men Women

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.

  • Evolution of Mean Wages(1980-2010)

    Men Women

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.

  • STEM Wage Premia

    Men Women

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.

  • Canonical model: Introduction

    • Framework to explain rise in between-group wage inequality by supply and demand factors

    • (Still) extensively used in the literature on the returns to education:

    • US: Katz and Murphy (1992), Juhn et al. 1993, Bound and Johnson (1998), Card and Lemieux (2001), Goldin and Katz (2007), Autor and Acemoglu (2011)

    • GER: Dustmann et al. (2009), Glitz and Wissmann (2016)

    • We use a modified version for STEM/Non-STEM categories instead of low- and high-skilled

  • Modifies specification à la Goldin and Katz 2007

    • CES production function for aggregate output Qt with two factors (STEM and Non-STEM workers):

    • LSt and LNt: STEM and Non-STEM labor supply employed in year t (measured in efficiency units)

    • α: Technology parameter indexing share of work allocated to STEM labor

    • at and bt: STEM/Non-STEM labor augmenting technological change

    • ρ where σ = 1/(1 – ρ) ∈ [0, ∞]: σ is aggregate elasticity of substitution between STEM and Non-STEM labor

  • Estimation Equation

    • Imposing that each labor input is paid its marginal product yields :

    • Linear trend in STEM-biased technological change:

    • Equation estimated by OLS:

    -> Identification relies on labor supply to be predetermined

  • STEM/Non-STEM log relative supply

    .4

    .425

    .45

    .475

    .5

    .525

    .55

    .575

    .6

    ST

    EM

    vs.

    No

    n-S

    TE

    M

    1980 1985 1990 1995 2000 2005 2010Year

    -2.5

    -2-1

    .5-1

    ST

    EM

    vs.

    No

    n-S

    TE

    M

    1980 1985 1990 1995 2000 2005 2010Year

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.

    Composition-adjusted log daily wage ratio

  • Detrended changes in STEM/Non-STEM

    relative supply and relative wage

    -.06

    -.04

    -.02

    0.0

    2

    Lo

    g c

    hang

    e r

    ela

    tive s

    upp

    ly

    -.03

    -.02

    -.01

    0

    .01

    .02

    .03

    Lo

    g c

    hang

    e r

    ela

    tive w

    age

    1980 1990 2000 2010Year

    Log change relative wage Log change relative supply

    .4

    .42

    5.4

    5.4

    75

    .5

    .52

    5.5

    5.5

    75

    .6

    1980 1990 2000 2010Jahr

    premium Fitted values

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.

    Observed STEM wage premium and model

    prediction

  • Regression Results

    (1) (2)

    STEM/Non-STEM -0.469** -0.586**

    relative supply 0.148 0.212

    Time 0.008*** 0.011**

    0.002 0.003

    Constant -0.608 -0.866

    0.335 0.476

    Observations 31 31

    Adj. R-Square 0.674 0.687

    Note: (1) Ful l -and part time with tra inees and

    multiple worker-year spel ls weighted by days

    (sample fol lowing Gl i tz and Wissmann 2016) (2) Ful l -

    time without tra inees and s ingle worker-year spel l

    (sample as in other analyses )

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.

  • Comparison with Other Studies

    Study Years Country Premium Estimate σ

    This study 1980-2010 GER STEM/Non-STEM -0.586** to -0.469** 1.71 to 2.13

    relative supply

    Glitz & 1980-2008 GER Medium-to-high -0.142*** 7.04

    Wissmann (2016) skilled rel. supply* (0.017)

    Acemoglu 1963-2008 USA College/High-school -0.644*** 1.55

    & Autor (2011) relative supply (0.066)

    Goldin & 1915-2005 USA College/High-school -0.611*** 1.64

    Katz (2007) relative supply

    * Age-group speci fic

  • Abowd-Kramarz-Margolis (1999) Decomposition Approach

    : Worker effect

    (earnings power portable across employers— often interpreted as a worker’s productivity)

    : Firm effect, where J(i,t) is an index function that indicatesidentity of workplace of individual i in period t

    (captures pay premium/discount common to all employees atworkplace j – often interpreted as a firm’s productivity)

    : Vector of individual-level controls (reflects changes in portable component in a worker’s earnings power)

  • Distribution of Worker Effects by Non-STEM/STEM

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.

  • Distribution of Firm Effects by Non-STEM/STEM

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.

  • Insourcing and Outsourcing(Men)

    FCSL: Food, Cleaning, Security, Logistics à la Goldschmidtand Schmieder, (2017)

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.

  • Insourcing and Outsourcing(Women)

    FCSL: Food, Cleaning, Security, Logistics à la Goldschmidtand Schmieder, (2017)

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.

  • Decomposition of STEM Premia

    Unadj. STEM premium1 Worker effect Firm effect Worker characteristics

    1 Minus residual STEM premium from `full model‘ estimation

  • Decomposition Results

    (1) (2) (3) (4) (5) (6)

    1985-1991 2002-2009 Δ Total 1985-1991 2002-2009 Δ Total

    Unadjusted STEM wage premium 0.41 0.49 0.08 0.34 0.41 0.07

    Component attributable to…

    Covariate index (Xβ) 0.05 0.03 -0.02 0.00 -0.01 0.00

    11.2 5.4 -25.0 -0.5 -1.7 -6.7

    Worker effects (α) 0.32 0.36 0.04 0.27 0.31 0.04

    76.8 72.4 49.8 79.1 76.7 64.7

    Firm effects (ψ) 0.05 0.11 0.06 0.07 0.10 0.03

    12.0 22.2 75.2 21.5 24.9 42.0

    Residual 0.00 0.00 0.00 0.02 0.01 -0.01

    Observations 1,642,515 1,751,402 769,537 833,517

    Men Women

    Data: IAB Sample-of-Integrated-Labor-Market-Biographies Regional-File, 1975-2010.

  • Conclusion

    • Positive STEM premium, with acceleration since mid-1990s for both men and women

    • Evidence for ‚shortage of STEM-skills’ being part of the increase in STEM wage premia growth

    • STEM workers are high productivity types of workers, but part of the increase in the premia over time driven by the quality of jobs STEM workers can obtain

    • STEM occupations are the counterpart of the domestically `outsourced’ occupations, i.e. they are insourced by high-productivity types of firms